Statistical Modeling of the Default Mode Brain Network Reveals a Segregated Highway Structure

Paul E. Stillman, James D. Wilson, Matthew J. Denny, Bruce A. Desmarais, Jr., Shankar Bhamidi, Skyler J. Cranmer, Zhong Lin Lu

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

We investigate the functional organization of the Default Mode Network (DMN)- A n important subnetwork within the brain associated with a wide range of higher-order cognitive functions. While past work has shown the whole-brain network of functional connectivity follows small-world organizational principles, subnetwork structure is less well understood. Current statistical tools, however, are not suited to quantifying the operating characteristics of functional networks as they often require threshold censoring of information and do not allow for inferential testing of the role that local processes play in determining network structure. Here, we develop the correlation Generalized Exponential Random Graph Model (cGERGM)- A statistical network model that uses local processes to capture the emergent structural properties of correlation networks without loss of information. Examining the DMN with the cGERGM, we show that, rather than demonstrating small-world properties, the DMN appears to be organized according to principles of a segregated highway-suggesting it is optimized for function-specific coordination between brain regions as opposed to information integration across the DMN. We further validate our findings through assessing the power and accuracy of the cGERGM on a testbed of simulated networks representing various commonly observed brain architectures.

Original languageEnglish (US)
Article number11694
JournalScientific reports
Volume7
Issue number1
DOIs
StatePublished - Dec 1 2017

Fingerprint

Brain
Statistical Models
Cognition
Power (Psychology)

All Science Journal Classification (ASJC) codes

  • General

Cite this

Stillman, Paul E. ; Wilson, James D. ; Denny, Matthew J. ; Desmarais, Jr., Bruce A. ; Bhamidi, Shankar ; Cranmer, Skyler J. ; Lu, Zhong Lin. / Statistical Modeling of the Default Mode Brain Network Reveals a Segregated Highway Structure. In: Scientific reports. 2017 ; Vol. 7, No. 1.
@article{74b974abcca542cea53e3c2f3d95b8a7,
title = "Statistical Modeling of the Default Mode Brain Network Reveals a Segregated Highway Structure",
abstract = "We investigate the functional organization of the Default Mode Network (DMN)- A n important subnetwork within the brain associated with a wide range of higher-order cognitive functions. While past work has shown the whole-brain network of functional connectivity follows small-world organizational principles, subnetwork structure is less well understood. Current statistical tools, however, are not suited to quantifying the operating characteristics of functional networks as they often require threshold censoring of information and do not allow for inferential testing of the role that local processes play in determining network structure. Here, we develop the correlation Generalized Exponential Random Graph Model (cGERGM)- A statistical network model that uses local processes to capture the emergent structural properties of correlation networks without loss of information. Examining the DMN with the cGERGM, we show that, rather than demonstrating small-world properties, the DMN appears to be organized according to principles of a segregated highway-suggesting it is optimized for function-specific coordination between brain regions as opposed to information integration across the DMN. We further validate our findings through assessing the power and accuracy of the cGERGM on a testbed of simulated networks representing various commonly observed brain architectures.",
author = "Stillman, {Paul E.} and Wilson, {James D.} and Denny, {Matthew J.} and {Desmarais, Jr.}, {Bruce A.} and Shankar Bhamidi and Cranmer, {Skyler J.} and Lu, {Zhong Lin}",
year = "2017",
month = "12",
day = "1",
doi = "10.1038/s41598-017-09896-6",
language = "English (US)",
volume = "7",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "Nature Publishing Group",
number = "1",

}

Statistical Modeling of the Default Mode Brain Network Reveals a Segregated Highway Structure. / Stillman, Paul E.; Wilson, James D.; Denny, Matthew J.; Desmarais, Jr., Bruce A.; Bhamidi, Shankar; Cranmer, Skyler J.; Lu, Zhong Lin.

In: Scientific reports, Vol. 7, No. 1, 11694, 01.12.2017.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Statistical Modeling of the Default Mode Brain Network Reveals a Segregated Highway Structure

AU - Stillman, Paul E.

AU - Wilson, James D.

AU - Denny, Matthew J.

AU - Desmarais, Jr., Bruce A.

AU - Bhamidi, Shankar

AU - Cranmer, Skyler J.

AU - Lu, Zhong Lin

PY - 2017/12/1

Y1 - 2017/12/1

N2 - We investigate the functional organization of the Default Mode Network (DMN)- A n important subnetwork within the brain associated with a wide range of higher-order cognitive functions. While past work has shown the whole-brain network of functional connectivity follows small-world organizational principles, subnetwork structure is less well understood. Current statistical tools, however, are not suited to quantifying the operating characteristics of functional networks as they often require threshold censoring of information and do not allow for inferential testing of the role that local processes play in determining network structure. Here, we develop the correlation Generalized Exponential Random Graph Model (cGERGM)- A statistical network model that uses local processes to capture the emergent structural properties of correlation networks without loss of information. Examining the DMN with the cGERGM, we show that, rather than demonstrating small-world properties, the DMN appears to be organized according to principles of a segregated highway-suggesting it is optimized for function-specific coordination between brain regions as opposed to information integration across the DMN. We further validate our findings through assessing the power and accuracy of the cGERGM on a testbed of simulated networks representing various commonly observed brain architectures.

AB - We investigate the functional organization of the Default Mode Network (DMN)- A n important subnetwork within the brain associated with a wide range of higher-order cognitive functions. While past work has shown the whole-brain network of functional connectivity follows small-world organizational principles, subnetwork structure is less well understood. Current statistical tools, however, are not suited to quantifying the operating characteristics of functional networks as they often require threshold censoring of information and do not allow for inferential testing of the role that local processes play in determining network structure. Here, we develop the correlation Generalized Exponential Random Graph Model (cGERGM)- A statistical network model that uses local processes to capture the emergent structural properties of correlation networks without loss of information. Examining the DMN with the cGERGM, we show that, rather than demonstrating small-world properties, the DMN appears to be organized according to principles of a segregated highway-suggesting it is optimized for function-specific coordination between brain regions as opposed to information integration across the DMN. We further validate our findings through assessing the power and accuracy of the cGERGM on a testbed of simulated networks representing various commonly observed brain architectures.

UR - http://www.scopus.com/inward/record.url?scp=85029504269&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85029504269&partnerID=8YFLogxK

U2 - 10.1038/s41598-017-09896-6

DO - 10.1038/s41598-017-09896-6

M3 - Article

VL - 7

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

IS - 1

M1 - 11694

ER -